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• W3044708963 abstract "We present a novel analysis of the expected risk of weighted majority vote in multiclass classification. The analysis takes correlation of predictions by ensemble members into account and provides a bound that is amenable to efficient minimization, which yields improved weighting for the majority vote. We also provide a specialized version of our bound for binary classification, which allows to exploit additional unlabeled data for tighter risk estimation. In experiments, we apply the bound to improve weighting of trees in random forests and show that, in contrast to the commonly used first order bound, minimization of the new bound typically does not lead to degradation of the test error of the ensemble." @default.
• W3044708963 created "2020-07-29" @default.
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• W3044708963 date "2020-07-01" @default.
• W3044708963 modified "2023-09-27" @default.
• W3044708963 title "Second Order PAC-Bayesian Bounds for the Weighted Majority Vote" @default.
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• W3044708963 hasPublicationYear "2020" @default.
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